Email this article Efficiency of Text Mining of Accident Narratives By Accessing Predictive Performance
Abstract
This work portrays the utilization of content mining with a mix of methods to naturally find accident attributes that can educate a superior comprehension of the supporters of the accidents. The review assesses the viability of content mining of mischance stories by evaluating prescient execution for the expenses of outrageous accidents. The outcomes demonstrate that prescient exactness for accident costs altogether enhances using highlights found by content mining and prescient precision additionally enhances using current outfit strategies. Critically, this review likewise appears through case illustrations how the discoveries from content mining of the stories can enhance comprehension of the supporters of rail accidents in ways unrealistic through just settled field investigation of the accident reports.
References
“Railroad safety statistics—2009 Annual report—Final,” Federal Railroad Admin., Washington, DC, USA, Apr. 2011. [Online]. Available: http://safetydata.fra.dot.gov/OfficeofSafety/publicsite/Publications.aspx
“Office of safety analysis,” Federal Railroad Administration, Washington, DC, USA, Oct. 2009. [Online]. Available: http://safetydata.fra.dot.gov/officeofsafety/
G. Cirovic and D. Pamucar, “Decision support model for prioritizing railway level crossings for safety improvements: Application of the adaptive neuro-fuzzy system,” Expert Syst. Appl., vol. 40, pp. 2208–2223, 2013.
L.-S. Tey, G. Wallis, S. Cloete, and L. Ferreira, “Modelling driver behaviour towards innovative warning devices at railway level crossings,” Neural Comput. Appl., vol. 51, pp. 104–111, Mar. 2013.
D. Akin and B. Akbas, “A neural network (NN) model to predict intersection crashes based upon driver, vehicle and roadway surface characteristics,” Sci. Res. Essays, vol. 5, pp. 2837–2847, 2010.
H. Gonzalez, J. Han, Y. Ouyang, and S. Seith, “Multidimensional data mining of traffic anomalies on large-scale road networks,” Transp. Res. Rec., vol. 2215, pp. 75–84, 2011.
E. D’Andrea, P. Ducange, B. Lazzerini, and F. Marcelloni, “Real-time detection of traffic from Twitter stream analysis,” IEEE Trans. Intell. Transp. Syst., vol. 16, no. 4, pp. 2269–2283, Mar. 2015.
F. Oliveira-Neto, L. Han, and M. K. Jeong, “An online self-learning algorithm for license plate matching,” IEEE Trans. Intell. Transp. Syst., vol. 14, no. 4, pp. 1806–1816, Dec. 2013.
J. Cao et al., “Web-based traffic sentiment analysis: Methods and applications,” IEEE Trans. Intell. Transp. Syst., vol. 15, no. 2, pp. 844–853, Apr. 2014.
J. Burgoonet al., “Detecting concealment of intent in transportation screening: A proof of concept,” IEEE Trans. Intell. Transp. Syst., vol. 10, no. 1, pp. 103–112, Mar. 2009.
Y. Zhao, T. H. Xu, andW. Hai-feng, “Text mining based fault diagnosis of vehicle on-board equipment for high speed railway,” in Proc. IEEE 17th Int. Conf. ITSC, Oct. 2014, pp. 900–905.
T. Hofmann, “Probabilistic latent semantic indexing,” in Proc. 22nd Annu. Int. ACM SIGIR Conf. Res. Develop. Inf. Retrieval, 1999, pp. 50–57.
R. Nayak, N. Piyatrapoomi, J. W. R. Nayak, N. Piyatrapoomi, and J. Weligamage, “Application of text mining in analysing road crashes for road asset management,” in Proc. 4th World Congr. Eng. Asset Manage., Athens, Greece, Sep. 2009, pp. 49–58.
“Leximancer Pty Ltd.” [Online]. Available: http://info.leximancer.com/ academic
A. E. Smith and M. S. Humphreys, “Evaluation of unsupervised semantic mapping of natural language with Leximancer concept mapping,” Behav. Res. Methods, vol. 38, no. 2, pp. 262–279, 2006.
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